We’ll look at Bayesian recommendation techniques that are being used by a large number of media companies today.

But this course isn’t just about news feeds.

Companies like Amazon, Netflix, and Spotify have been using recommendations to suggest products, movies, and music to customers for many years now.

These algorithms have led to billions of dollars in added revenue.

So I assure you, what you’re about to learn in this course is very real, very applicable, and will have a huge impact on your business.

For those of you who like to dig deep into the theory to understand how things really work, you know this is my specialty and there will be no shortage of that in this course. We’ll be covering state of the art algorithms like matrix factorization and deep learning (making use of both supervised andunsupervised learning), and you’ll learn a bag full of tricks to improve upon baseline results.

Whether you sell products in your e-commerce store, or you simply write a blog – you can use these techniques to show the right recommendations to your users at the right time.

If you’re an employee at a company, you can use these techniques to impress your manager and get a raise!

Note: this course is NOT a part of my deep learning series (it’s not Deep Learning part 11) because while it contains a major deep learning component, a lot of the course uses non-deep learning techniques as well. The deep learning parts apply modified neural network architectures and deep learning technologies to the recommender problem.

Simple Deep Learning for Programmers

Learn Deep Learning via Keras examples with absolutely no math

I’m always intrigued when students tell me they want to learn deep learning without doing any math.

I was explaining to someone just yesterday – if you look at <insert famous deep learning book by famous deep learning researcher here> – the entire thing is actually cover to cover equations. Ha!

Anyhow, I wanted to test this hypothesis. How far can one get, if they try to learn deep learning via an API?

So I made this little book. It’s full of Keras examples, starting from a basic feedforward neural network, then adding some modern techniques like dropout and batch norm, then moving to more advanced architectures like CNNs and RNNs.

Of course, if you are a reader of my newsletter, you probably aren’t afraid of math!

But, I thought I’d share this book with you anyway, since it contains some interesting examples that you haven’t seen in my courses before.

– CIFAR dataset
– time series prediction using an RNN
– machine translation using a Bidirectional RNN (not a seq-to-seq model as in my Advanced NLP course)

This would also be a great opportunity to brush up on your Keras skills, which are going to be useful for my next course (hopefully coming out in a few days!)

Finally – I’ve also linked below my related book, “Simple Machine Learning for Programmers” – it is a similar experiment in teaching about machine learning using an API with no math. It’s the same as the machine learning section of my Numpy course but I know some students like to have written versions of things so they can read on the subway / airplane. If so, check it out!

Deep Learning: Advanced Computer Vision

[Scroll down to the bottom for the coupon and important instructions for how to use it]

This is one of the most exciting courses I’ve done and it really shows how fast and how far deep learning has come over the years.

When I first started my deep learning series, I didn’t ever consider that I’d make two courses on convolutional neural networks.

I think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.

Let me give you a quick rundown of what this course is all about:

We’re going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as VGG, ResNet, and Inception (named after the movie which by the way, is also great!)

We’re going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots.

In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label.

You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time)

We’ll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors.

Another very popular computer vision task that makes use of CNNs is called neural style transfer.

This is where you take one image called the content image, and another image called the style image, and you combine these to make an entirely new image, that is as if you hired a painter to paint the content of the first image with the style of the other. Unlike a human painter, this can be done in a matter of seconds.

AWESOME FACTS:

One of the major themes of this course is that we’re moving away from the CNN itself, to systems involving CNNs.

Therefore, instead of focusing on the detailed inner workings of CNNs (which we’ve already done), we’ll focus on high-level building blocks. The result? Almost zero math.

Another result? No complicated low-level code such as that written in Tensorflow, Theano, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you.

The VIP Version

For this launch, I am offering a limited edition VIP version of the course. This offer will END in exactly 5 days (before next weekend!).

As usual, you MUST use the coupon IAMAVIP (automatically applied when you use the link below) to get the VIP version. If you do not use the IAMAVIP coupon, you will not get the VIP bonuses.

So, what do you get with the VIP version?

The final section of the course is on neural style transfer – however – a brand new section – one I may not release for a long time, if ever, is in the works.

This “hidden section” is on super resolution and fast neural style transfer (speeding up the original neural style transfer algorithm).

Super resolution is where you take a low-quality, small image, and turn it into a higher-quality, higher resolution image. It’s the stuff of science fiction and spy movies – now it’s real!

This new hidden section of the course will be provided to you in the form of a book chapter (PDF format) along with accompanying code which will NOT appear in the official course repo.

To get it, use the coupon below (automatically applied when you click the link):

Important!!!: Users have reported that the IAMAVIP coupon code may get overridden by Udemy’s own codes. When you checkout, you’ll want to make note of something like this:

Notice how it says “IAMAVIP is not applied”. As far as I know, there’s no way around this problem. However, note that you can ALWAYS get VIP material by signing up for the course at https://deeplearningcourses.com/c/advanced-computer-vision. If you purchase the course at deeplearningcourses.com, and you’d like to access the course on Udemy as well, just shoot me an email and I will give you a free coupon.

ALL Courses on Udemy $10.99

Deep Learning BLAST OFF 2018

Last Chance to meet your 2018 New Years Resolutions!

Earlier this year we had a New Years sale with all courses 90% off. I’ve been getting a lot of emails from those who missed it, those who just joined, etc. Don’t worry – you have one more chance to meet your New Years 2018 goals!

In 2018 and beyond, AI will continue to rise in importance, and the best jobs, the best new technologies – will absolutely depend on AI to have a competitive edge in the marketplace.

For the next 2 days, Udemy is extending its New Years sale, and ALL courses on the site are available for just $10.99!

For my courses, please use the coupons below (included in the links), or if you want, enter the coupon code: BLASTOFF2018.

For prerequisite courses and all other courses, follow the links at the bottom.